The deep learning models illustrated in this section are implemented using the deep learning library PyTorch [1]} on NVIDIA 1080 GPU. Adam optimizer [2]} is adopted with an initial learning rate of \(1 e^{-3}\)  and a decay rate of \(0.6\) . We set the \(\lambda \)  in Eq. (REF ) as \(1 e^{-6}\) . In particular, \(32 \times 32 \times 1024\)  patches are cropped randomly from the simulated cubes as the training inputs for deep learning models. The batch size is set as 4. We train the PRS-Net on data across different SBR conditions denoted by (10:2, 5:2, 2:2, 10:10, 5:10, 2:10, 10:50, 5:50, 2:50, 3:100, 2:100, 1:100).
